



"""simple docstring"""





import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  lowerCamelCase  			=		False

if is_vision_available():
  from PIL import Image

  from transformers import PixaStructImageProcessor
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def __init__(				self     :    Tuple						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Optional[Any]=7						,							_UpperCAmelCase     :    str=3						,							_UpperCAmelCase     :    Any=18						,							_UpperCAmelCase     :    int=30						,							_UpperCAmelCase     :    Tuple=400						,							_UpperCAmelCase     :    List[Any]=None						,							_UpperCAmelCase     :    str=True						,							_UpperCAmelCase     :    List[Any]=True						,							_UpperCAmelCase     :    int=None						,							)					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				size if size is not None else {"height": 20, "width": 20}
             UpperCAmelCase_     					=				parent
             UpperCAmelCase_     					=				batch_size
             UpperCAmelCase_     					=				num_channels
             UpperCAmelCase_     					=				image_size
             UpperCAmelCase_     					=				min_resolution
             UpperCAmelCase_     					=				max_resolution
             UpperCAmelCase_     					=				size
             UpperCAmelCase_     					=				do_normalize
             UpperCAmelCase_     					=				do_convert_rgb
             UpperCAmelCase_     					=				[512, 1024, 2048, 4096]
             UpperCAmelCase_     					=				patch_size if patch_size is not None else {"height": 16, "width": 16}



       def 	lowercase__							(				self     :    List[Any]       )					->		List[Any]:


             '''simple docstring'''




             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







       def 	lowercase__							(				self     :    List[Any]       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
             UpperCAmelCase_     					=				Image.open(requests.get(_UpperCAmelCase						,							stream=_UpperCAmelCase       ).raw       ).convert("RGB"       )
             return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,   reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''							,   )
@require_torch
@require_vision
class  lowercase__  (       SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	PixaStructImageProcessor if is_vision_available() else None



       def 	lowercase__							(				self     :    Optional[int]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				PixaStructImageProcessingTester(self       )



       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		Any:


             '''simple docstring'''




             return self.image_processor_tester.prepare_image_processor_dict()



       def 	lowercase__							(				self     :    Dict       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(_UpperCAmelCase						,							"do_normalize"       )       )
             self.assertTrue(hasattr(_UpperCAmelCase						,							"do_convert_rgb"       )       )



       def 	lowercase__							(				self     :    str       )					->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processor_tester.prepare_dummy_image()

             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             UpperCAmelCase_     					=				2048

             UpperCAmelCase_     					=				image_processor(_UpperCAmelCase						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       )
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,							torch.tensor(0.0606       )						,							atol=1e-3						,							rtol=1e-3       )       )



       def 	lowercase__							(				self     :    List[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             UpperCAmelCase_     					=				prepare_image_inputs(self.image_processor_tester						,							equal_resolution=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							Image.Image       )

             # Test not batched input
             UpperCAmelCase_     					=				(
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                   # Test not batched input
                   UpperCAmelCase_     					=				image_processor(
                       image_inputs[0]						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(1, max_patch, expected_hidden_dim)						,							)

                   # Test batched
                   UpperCAmelCase_     					=				image_processor(
                       _UpperCAmelCase						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,							)



       def 	lowercase__							(				self     :    str       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             UpperCAmelCase_     					=				prepare_image_inputs(self.image_processor_tester						,							equal_resolution=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							Image.Image       )

             # Test not batched input
             UpperCAmelCase_     					=				(
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             UpperCAmelCase_     					=				True

             for max_patch in self.image_processor_tester.max_patches:
                   # Test not batched input
                   with self.assertRaises(_UpperCAmelCase       ):
                         UpperCAmelCase_     					=				image_processor(
                             image_inputs[0]						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches

                   UpperCAmelCase_     					=				"Hello"

                   UpperCAmelCase_     					=				image_processor(
                       image_inputs[0]						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase						,							header_text=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(1, max_patch, expected_hidden_dim)						,							)

                   # Test batched
                   UpperCAmelCase_     					=				image_processor(
                       _UpperCAmelCase						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase						,							header_text=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,							)



       def 	lowercase__							(				self     :    str       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             UpperCAmelCase_     					=				prepare_image_inputs(self.image_processor_tester						,							equal_resolution=_UpperCAmelCase						,							numpify=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							np.ndarray       )

             UpperCAmelCase_     					=				(
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                   # Test not batched input
                   UpperCAmelCase_     					=				image_processor(
                       image_inputs[0]						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(1, max_patch, expected_hidden_dim)						,							)

                   # Test batched
                   UpperCAmelCase_     					=				image_processor(
                       _UpperCAmelCase						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,							)







       def 	lowercase__							(				self     :    Optional[int]       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             UpperCAmelCase_     					=				prepare_image_inputs(self.image_processor_tester						,							equal_resolution=_UpperCAmelCase						,							torchify=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							torch.Tensor       )

             # Test not batched input
             UpperCAmelCase_     					=				(
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                   # Test not batched input
                   UpperCAmelCase_     					=				image_processor(
                       image_inputs[0]						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(1, max_patch, expected_hidden_dim)						,							)

                   # Test batched
                   UpperCAmelCase_     					=				image_processor(
                       _UpperCAmelCase						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,							)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,   reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''							,   )
@require_torch
@require_vision
class  lowercase__  (       SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	PixaStructImageProcessor if is_vision_available() else None



       def 	lowercase__							(				self     :    List[str]       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				PixaStructImageProcessingTester(self						,							num_channels=4       )
             UpperCAmelCase_     					=				3



       @property
       def 	lowercase__							(				self     :    str       )					->		Optional[int]:


             '''simple docstring'''




             return self.image_processor_tester.prepare_image_processor_dict()



       def 	lowercase__							(				self     :    str       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(_UpperCAmelCase						,							"do_normalize"       )       )
             self.assertTrue(hasattr(_UpperCAmelCase						,							"do_convert_rgb"       )       )







       def 	lowercase__							(				self     :    List[str]       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             UpperCAmelCase_     					=				prepare_image_inputs(self.image_processor_tester						,							equal_resolution=_UpperCAmelCase       )
             for image in image_inputs:
                   self.assertIsInstance(_UpperCAmelCase						,							Image.Image       )

             # Test not batched input
             UpperCAmelCase_     					=				(
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * (self.image_processor_tester.num_channels - 1)
             ) + 2

             for max_patch in self.image_processor_tester.max_patches:
                   # Test not batched input
                   UpperCAmelCase_     					=				image_processor(
                       image_inputs[0]						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(1, max_patch, expected_hidden_dim)						,							)

                   # Test batched
                   UpperCAmelCase_     					=				image_processor(
                       _UpperCAmelCase						,							return_tensors="pt"						,							max_patches=_UpperCAmelCase       ).flattened_patches
                   self.assertEqual(
                       encoded_images.shape						,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,							)










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     						=				{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''RobertaPreLayerNormForCausalLM''',
        '''RobertaPreLayerNormForMaskedLM''',
        '''RobertaPreLayerNormForMultipleChoice''',
        '''RobertaPreLayerNormForQuestionAnswering''',
        '''RobertaPreLayerNormForSequenceClassification''',
        '''RobertaPreLayerNormForTokenClassification''',
        '''RobertaPreLayerNormModel''',
        '''RobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFRobertaPreLayerNormForCausalLM''',
        '''TFRobertaPreLayerNormForMaskedLM''',
        '''TFRobertaPreLayerNormForMultipleChoice''',
        '''TFRobertaPreLayerNormForQuestionAnswering''',
        '''TFRobertaPreLayerNormForSequenceClassification''',
        '''TFRobertaPreLayerNormForTokenClassification''',
        '''TFRobertaPreLayerNormMainLayer''',
        '''TFRobertaPreLayerNormModel''',
        '''TFRobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''FlaxRobertaPreLayerNormForCausalLM''',
        '''FlaxRobertaPreLayerNormForMaskedLM''',
        '''FlaxRobertaPreLayerNormForMultipleChoice''',
        '''FlaxRobertaPreLayerNormForQuestionAnswering''',
        '''FlaxRobertaPreLayerNormForSequenceClassification''',
        '''FlaxRobertaPreLayerNormForTokenClassification''',
        '''FlaxRobertaPreLayerNormModel''',
        '''FlaxRobertaPreLayerNormPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


